EXPERIMENTAL ANALYSIS AND MODELING OF SEQUENTIAL ROUTE CHOICE UNDER AN ADVANCED TRAVELER INFORMATION SYSTEM IN A SIMPLISTIC TRAFFIC NETWORK

Abstract

An experiment to collect sequential route choice data under the influence of an advanced traveler information system was performed using a personal computer-based simulation. The experiment collected information on drivers' pretrip route choice behavior at three levels of information accuracy: 60, 75, and 90%. An analysis of variance was performed on the data to investigate the interrelationships among the different variables in an attempt to develop an understanding of what factors significantly influence route choice behavior and learning. An attempt was made to model sequential route choice behavior using a binary logit model formulation; the results were mixed. It was assumed that drivers update their knowledge of the system on the basis of their previous experiences; therefore an information updating function was specified and incorporated into the model. The results indicate that drivers can rapidly identify the accuracy level of information and that they adjust their behavior accordingly. Evidence also indicates that an accuracy threshold level exists below which drivers will not follow advice and above which drivers readily follow advice. It was found that male subjects agreed with advice more often than females, that less experienced drivers agreed more often than experienced drivers, and that a bias exists with drivers much more willing to follow advice to take a freeway route. The model of route choice behavior had a prediction rate that was 79% accurate, which also indicated that previous experiences had little effect on current route choices. This value may be the result of a misspecified updating function, indicating that further research is required to identify these learning relationships.